class_docstr: Creates a Dataset for T5/BART architecture based Generative QA 
class_docstr_tok: ["Creates","a","Dataset","for","T5\/BART","architecture","based","Generative","QA"]
code: def __getitem__(self, idx: int):
      feature = self.features[idx]
      if self.mode == INFERENCE_MODE:
          return (
              np.array(feature.input_ids),
              np.array(feature.input_attn_mask),
              np.array(feature.unique_id),
          )
      else:
          return (
              np.array(feature.input_ids),
              np.array(feature.input_attn_mask),
              np.array(feature.unique_id),
              np.array(feature.labels),
          )
  
file_path: NeMo/nemo/collections/nlp/data/question_answering/dataset/qa_s2s_dataset.py
imports: import os
  import numpy
  import torch
  from tqdm import trange
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import INFERENCE_MODE, TRAINING_MODE
  from nemo.collections.nlp.data.question_answering.dataset.qa_dataset import QADataset
  from nemo.collections.nlp.data.question_answering.input_example.qa_s2s_input_example import S2SQAInputExample
  from nemo.utils import logging
label_desc: Use image node_id 8 for calling the S2SQADataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: np,  np,  np, np,  np,  np,  np